Prompt engineering focuses on the words used in one request. Context engineering designs everything the model receives while doing a task. That can include instructions, source files, tests, documentation, tool results, conversation history, and summaries.

A strong prompt cannot fix missing or misleading context. If the model cannot see the current interface or project rule, it must guess. Context engineering makes the right information available at the right time and removes noise that could distract the model. It also covers how context is retrieved, ordered, updated, and carried between sessions. In practice, prompt writing is one part of the larger job of building a reliable information environment for the assistant.

Instruction files store standing guidance for coding assistants. They can describe repository layout, build and test commands, coding conventions, safety limits, and the expected definition of done. Tools load them automatically at the relevant project or directory scope.

They solve the problem of repeating stable information in every prompt. Without them, each session may guess how to run tests or which helper to use. Keep the guidance short, direct, and specific. Link to longer documentation instead of copying it all. Never put secrets in these files. Review changes like code because stale or conflicting instructions can make an agent less reliable than having no rule at all.

Include stable facts that affect many tasks. Examples include important directories, standard commands, architectural boundaries, naming rules, approved libraries, and dangerous actions that need approval. Add a concise definition of done, such as running focused tests and reporting any failures.

Leave out secrets, credentials, customer data, temporary task details, and long tutorials. Do not copy information that already has a trusted source; point to that document instead. Avoid vague advice such as “write good code,” because it gives the model no useful action. Also remove rules that tools or linters already enforce unless the assistant needs them to choose an approach. A useful instruction file is a small map and policy guide, not a complete project handbook.

A context window is the amount of text and other tokenized information a model can consider in one request. It contains the prompt, prior conversation, instructions, source files, tool output, and the model’s own response. Every model has a limit.

When the window fills, older content may be removed or summarized, and important details can be lost. Even before the limit, too much unrelated material can make the model overlook the right fact. Give the assistant current, relevant sources instead of a repository dump. For long tasks, record decisions and progress in a compact handoff. A larger window helps with broad work, but careful context selection still improves accuracy, cost, and speed.

Nearly everything the agent reads or writes consumes tokens. This includes system and project instructions, your messages, earlier replies, source files, search results, command output, error logs, documentation, diffs, tool definitions, and summaries. Repeated content can be charged and processed again unless caching applies.

Large test logs, generated files, and broad searches can consume the window quickly without adding much value. Ask for focused output, open only relevant file sections, and filter noisy command results. Keep standing rules lean because they appear in many requests. When a task changes direction, start a fresh session instead of carrying unrelated history. Token awareness is not only about cost; it protects the model’s attention for the evidence that matters.

Rules files are loaded often, so every unnecessary paragraph uses context on many requests. Long files also make important guidance harder to notice. Conflicting or overly detailed rules can cause the assistant to follow the wrong instruction or spend time reconciling advice that does not apply.

Keep only stable, high-impact facts that change how work should be done. Use clear commands, short examples, and links to deeper documentation. Move subsystem guidance into scoped files when supported. Remove rules that a formatter, type checker, or test already enforces automatically. Review the file after repeated agent mistakes and after architecture changes. Lean rules reduce cost, improve attention, and are easier for people to keep accurate.

An AI-legible repository makes its structure and intent easy for an assistant to discover. It has clear names, small focused modules, typed interfaces, current documentation, reliable tests, and standard commands. Important behavior is written down instead of living only in team memory.

This also helps human developers. An agent can find the owning module, inspect a nearby example, run one documented test command, and understand what success means. Hidden setup, duplicate patterns, vague names, and stale docs force it to guess. Improve legibility by making boundaries explicit, removing dead code, documenting unusual decisions, and keeping examples current. The goal is not to write for a model alone; it is to make project knowledge visible and verifiable.